def testGradOnUnsupportedType(self):
in_shape = [1, 4, 6, 1]
out_shape = [1, 2, 3, 1]
x = np.arange(0, 24).reshape(in_shape).astype(np.uint8)
with self.test_session():
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bicubic(input_tensor, out_shape[1:3])
grad = gradients_impl.gradients(input_tensor, [resize_out])
self.assertEqual([None], grad)
def testGradOnUnsupportedType(self):
in_shape = [1, 4, 6, 1]
out_shape = [1, 2, 3, 1]
x = np.arange(0, 24).reshape(in_shape).astype(np.uint8)
with self.cached_session():
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bicubic(input_tensor, out_shape[1:3])
grad = gradients_impl.gradients(input_tensor, [resize_out])
self.assertEqual([None], grad)
def testGradOnUnsupportedType(self, use_tape):
with test_util.AbstractGradientTape(use_tape=use_tape) as tape:
in_shape = [1, 4, 6, 1]
out_shape = [1, 2, 3, 1]
x = np.arange(0, 24).reshape(in_shape).astype(np.uint8)
input_tensor = constant_op.constant(x, shape=in_shape)
tape.watch(input_tensor)
resize_out = image_ops.resize_bicubic(input_tensor, out_shape[1:3])
with self.cached_session():
grad = tape.gradient(resize_out, [input_tensor])
self.assertEqual([None], grad)
def testGradFromResizeToSmallerInBothDims(self):
in_shape = [1, 4, 6, 1]
out_shape = [1, 2, 3, 1]
x = np.arange(0, 24).reshape(in_shape).astype(np.float32)
for align_corners in [True, False]:
with self.test_session():
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bicubic(input_tensor, out_shape[1:3],
align_corners=align_corners)
err = gradient_checker.compute_gradient_error(
input_tensor, in_shape, resize_out, out_shape, x_init_value=x)
self.assertLess(err, 1e-3)
def testShapeIsCorrectAfterOp(self):
in_shape = [1, 2, 2, 1]
out_shape = [1, 4, 6, 1]
x = np.arange(0, 4).reshape(in_shape).astype(np.float32)
for align_corners in [True, False]:
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bicubic(
input_tensor, out_shape[1:3], align_corners=align_corners)
with self.cached_session():
self.assertEqual(out_shape, list(resize_out.get_shape()))
resize_out = self.evaluate(resize_out)
self.assertEqual(out_shape, list(resize_out.shape))
def testGradFromResizeToSmallerInBothDims(self):
in_shape = [1, 4, 6, 1]
out_shape = [1, 2, 3, 1]
x = np.arange(0, 24).reshape(in_shape).astype(np.float32)
for align_corners in [True, False]:
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bicubic(
input_tensor, out_shape[1:3], align_corners=align_corners)
with self.cached_session():
err = gradient_checker.compute_gradient_error(
input_tensor, in_shape, resize_out, out_shape, x_init_value=x)
self.assertLess(err, 1e-3)
def testShapeIsCorrectAfterOp(self):
in_shape = [1, 2, 2, 1]
out_shape = [1, 4, 6, 1]
x = np.arange(0, 4).reshape(in_shape).astype(np.float32)
for align_corners in [True, False]:
with self.test_session() as sess:
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bicubic(input_tensor, out_shape[1:3],
align_corners=align_corners)
self.assertEqual(out_shape, list(resize_out.get_shape()))
resize_out = sess.run(resize_out)
self.assertEqual(out_shape, list(resize_out.shape))
def func(input_tensor, align_corners=align_corners):
return image_ops.resize_bicubic(input_tensor,
out_shape[1:3],
align_corners=align_corners)
